Traditional gene expression techniques (e.g. microarray analyses) have already uncovered functional categories of genes, such as splice variants of ion channels or genes involved in innate immune/inflammatory responses, that may contribute to alcohol dependence in humans. Microarrays produce long lists of individual candidate genes, but have not shed light on how these genes might interact to produce an alcohol-dependent phenotype. Analyzing gene co-expression networks from alcoholic brain tissue generates a more integrated, systems-level view, spanning multiple cell types or brain regions (Fig. 3). Gene networks have also highlighted a novel role for transcriptional regulation in development of alcohol dependence via epigenetic mechanisms and non-coding RNAs (Fig. 1). Bioinformatics analyses and sequencing techniques, such as RNA-seq, now permit unparalleled insight into the complexity of the transcriptome. Although still in the early stages of use, RNA-seq has been used to determine spatial organization of the transcriptome (Lee et al., 2014) and extract some of the subtle expression differences induced by individual neurons and their microenvironments (Lovatt et al., 2014). This and other rapidly advancing computational approaches promise to improve discovery of the
